"Space Situational Awareness (SSA) knowledge is crucial for the safe operation of space activities. One of the challenges in SSA is the increasing number of objects that require continuous detection and tracking. Fully automatic optical systems that leverage Machine Learning (ML) models to detect and track Resident Space Objects (RSOs) will help address this challenge. Such systems would also enhance catalogue maintenance and manoeuvre detection capabilities. Leveraging the recent advancements in ML, a state-of-the-art model is proposed that uses the data from RMIT University’s Robotic Optical Observatory (ROO) for detecting RSOs in geosynchronous orbit. The proposed ML model uses Feature Pyramid Network (FPN) - a deep feature extractor that takes a single-scale image of arbitrary size as input, and outputs proportionally sized feature maps at multiple levels. In this study, the ML model’s ability to identify RSOs in ROO’s imagery is tested and compared against ROO’s existing RSO identification software. It is intended that the ML model will be incorporated into ROO’s real-time observation campaigns, and that the SSA data collected will be made publicly available to the SSA community."